WO2014/005783 (Ref: FN-384-PCT) discloses a method for correcting a distorted input image comprising determining a local region of an image to be displayed and dividing the region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within the input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of the input image sufficient to span the boundary of the distorted tile are determined. Successive rows of the distorted input from between the maximum and minimum addresses are read. Distortion of the non-rectangular portion of said distorted input image is corrected to provide a tile of a corrected output image which is stored.
Once a distorted input image has been corrected, if such distortion correction is required, object detection such as face detection can be performed to identify one or more regions of interest (ROI) within the image which can be of use for subsequent or further image processing. For example, exposure levels or focus distance for subsequent image acquisition can be based on a detected object or objects.
Note that depending on the type of object detection, tracking and/or classification being employed, any ROI detected within a (corrected) image can bound objects in a number of different orientations and having a number of different sizes.
Apart from for example, adjusting exposure or focus, it is also possible to perform further processing on any detected ROI. For example, when a ROI including a face is detected, it can be desirable to perform face recognition in order to identify an imaged individual.
Before performing such further processing, it can be useful to provide a normalised version of the ROI so that each normalised ROI submitted for further processing is in an orthogonal orientation and is of a given size (or one of a limited number of sizes). This, for example, allows classifiers or further processing modules operating on the normalised version of the ROI to be more readily implemented in hardware and so provides improved further processing.
In order to do so, the ROI needs to be re-sampled to produce the normalised version of the ROI.
Richard Szeliski, Simon Winder, and Matt Uyttendaele, “High-quality multi-pass image resampling”, MSR-TR-2010-10, Microsoft Technical Report, February 2010 discloses a family of multi-pass image resampling algorithms that use one-dimensional filtering stages to achieve high-quality results at low computational cost. Frequency-domain analysis is performed to ensure that very little aliasing occurs at each stage in the multi-pass transform and to insert additional stages where necessary to ensure this. Using one-dimensional resampling enables the use of small resampling kernels, thus producing highly efficient algorithms.
It is an object of the present invention to provide an improved technique for normalising a ROI from within an image.